In-vivo quantification of lactate using Near Infrared reflectance spectroscopy.

Elevated lactate levels in blood (hyperlactatemia) are indications of hypoperfusion or sepsis in critical care conditions. Quantification and monitoring of this important marker is performed using intermittent blood sampling, which fails to provide a complete scenario to aid clinicians in diagnosis. The feasibility of Near Infrared (NIR) Spectroscopy as an alternative to state-of-the-art techniques in critical care environments for non-invasive and continuous monitoring of lactate has previously been established. Nevertheless, the challenge lies in translating this research from bench to bedside monitoring. For this reason, a pilot investigation was carried out with a portable NIR spectrometer, where spectra in the range of 900-1300 nm were collected from 8 healthy human volunteers undertaking a high intensity incremental exercise protocol for lactate monitoring. This paper reports on the measurement set-up, spectra acquisition and analysis of diffuse NIR reflectance spectra of varying concentrations of lactate. The results obtained by 2D correlation analysis and linear regression are promising and show that the wavelengths 923 nm, 1047 nm, 1142 nm, 1233 nm, 1280 nm and 1330 nm are significant for lactate concentration determination in the NIR region. This provides the necessary confidence for using NIR sensor technology for lactate detection in critical care.

An empirical investigation of deviations from the Beer-Lambert law in optical estimation of lactate.

The linear relationship between optical absorbance and the concentration of analytes-as postulated by the Beer-Lambert law-is one of the fundamental assumptions that much of the optical spectroscopy literature is explicitly or implicitly based upon. The common use of linear regression models such as principal component regression and partial least squares exemplifies how the linearity assumption is upheld in practical applications. However, the literature also establishes that deviations from the Beer-Lambert law can be expected when (a) the light source is far from monochromatic, (b) the concentrations of analytes are very high and (c) the medium is highly scattering. The lack of a quantitative understanding of when such nonlinearities can become predominant, along with the mainstream use of nonlinear machine learning models in different fields, have given rise to the use of methods such as random forests, support vector regression, and neural networks in spectroscopic applications. This raises the question that, given the small number of samples and the high number of variables in many spectroscopic datasets, are nonlinear effects significant enough to justify the additional model complexity? In the present study, we empirically investigate this question in relation to lactate, an important biomarker. Particularly, to analyze the effects of scattering matrices, three datasets were generated by varying the concentration of lactate in phosphate buffer solution, human serum, and sheep blood. Additionally, the fourth dataset pertained to invivo, transcutaneous spectra obtained from healthy volunteers in an exercise study. Linear and nonlinear models were fitted to each dataset and measures of model performance were compared to attest the assumption of linearity. To isolate the effects of high concentrations, the phosphate buffer solution dataset was augmented with six samples with very high concentrations of lactate between (100-600 mmol/L). Subsequently, three partly overlapping datasets were extracted with lactate concentrations varying between 0-11, 0-20 and 0-600 mmol/L. Similarly, the performance of linear and nonlinear models were compared in each dataset. This analysis did not provide any evidence of substantial nonlinearities due high concentrations. However, the results suggest that nonlinearities may be present in scattering media, justifying the use of complex, nonlinear models.

The efficacy of support vector machines in modelling deviations from the Beer-Lambert law for optical measurement of lactate.

Lactate is an important biomarker with a significant diagnostic and prognostic ability in relation to life-threatening conditions and diseases such as sepsis, diabetes, cancer, pulmonary and kidney diseases, to name a few. The gold standard method for the measurement of lactate relies on blood sampling, which due to its invasive nature, limits the ability of clinicians in frequent monitoring of patients' lactate levels. Evidence suggests that the optical measurement of lactate holds promise as an alternative to blood sampling. However, achieving this aim requires better understanding of the optical behavior of lactate. The present study investigates the potential deviations of absorbance from the Beer-Lambert law in high concentrations of lactate. To this end, a number of nonlinear models namely support vector machines with quadratic, cubic and quartic kernels and radial basis function kernel are compared with the linear principal component regression and linear support vector machine. Interestingly, it is shown that even in extremely high concentrations of lactate (600 mmol/L) in a phosphate buffer solution, the linear models surpass the performance of the other models.

Near Infrared and Aquaphotomic analysis of water absorption in lactate containing media.

Increased concentrations of lactate levels in blood are often seen in patients with life-threatening cellular hypoperfusion or infections. State-of-the-art techniques used in clinical practice for measuring serum lactate concentrations rely on intermittent blood sampling and do not permit continuous monitoring of this all important parameter in critical care environments.In recent years, Near Infrared (NIR) Spectroscopy has been established as a possible alternative to existing methods that can mitigate these constraints and be used for non-invasive continuous monitoring of lactate. Nevertheless, the dominant absorption of -OH overtone bands of water in the NIR presents a challenge and complicates the accurate detection of other absorbers such as lactate. For this reason, comprehensive analysis of the -OH overtone bands with systematic lactate concentration changes is essential. This paper reports on the analysis of NIR spectra of two aqueous systems of varying concentrations of lactate in saline and whole blood using the principles of Aquaphotomics.The results show distinctive conformational and structural differences in lactate-water binding, which arise due to the molecular interactions of bonds present in respective solvents.

In vitro quantification of lactate in Phosphate Buffer Saline (PBS) samples.

Continuous measurement of lactate levels in the blood is a prerequisite in intensive care patients who are susceptible to sepsis due to their suppressed immune system and increased metabolic demand. Currently, there exists no noninvasive tool for continuous measurement of lactate in clinical practice. The current mode of measurement is based on arterial blood gas analyzers which require sampling of arterial blood. In this work, we propose the use of Near Infra-Red (NIR) spectroscopy together with multivariate models as a means to non-invasively predict the concentration of lactate in the blood. As the first step towards this objective, we examined the possibility of accurately predicting concentrations of sodium lactate (NaLac) from the NIR spectra of 37 isotonic phosphate buffer saline (PBS) samples containing NaLac ranging from 0 to 20 mmol/L. NIR spectra of PBS samples were collected using the Lambda 1050 dual beam spectrometer over a spectral range of 800 - 2600 nm with a quartz cell of 1 mm optical path. Estimates and calibration of the lactate concentration with the NIR spectra were made using Partial Least-Squares (PLS) regression analysis and leave-one-out cross-validation on filtered spectra. The regression analysis showed a correlation coefficient of 0.977 and a standard error of 0.89 mmol/L between the predicted and prepared samples. The results suggest that NIR spectroscopy together with multivariate models can be a valuable tool for non-invasive assessment of blood lactate concentrations.

Comparison of a Genetic Algorithm Variable Selection and Interval Partial Least Squares for quantitative analysis of lactate in PBS.

Blood lactate is an important biomarker that has been linked to morbidity and mortality of critically ill patients, acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the clinical measurement of blood lactate is done by collecting intermittent blood samples. Therefore, noninvasive, optical measurement of this significant biomarker would lead to a big leap in healthcare. This study, presents a quantitative analysis of the optical properties of lactate. The benefits of wavelength selection for the development of accurate, robust, and interpretable predictive models have been highlighted in the literature. Additionally, there is an obvious, time- and cost-saving benefit to focusing on narrower segments of the electromagnetic spectrum in practical applications. To this end, a dataset consisting of 47 spectra of Na-lactate and Phosphate Buffer Solution (PBS) was produced using a Fourier transform infrared spectrometer, and subsequently, a comparative study of the application of a genetic algorithm-based wavelength selection and two interval selection methods was carried out. The high accuracy of predictions using the developed models underlines the potential for optical measurement of lactate. Moreover, an interesting finding is the emergence of local features in the proposed genetic algorithm, while, unlike the investigated interval selection methods, no explicit constraints on the locality of features was imposed. Finally, the proposed genetic algorithm suggests the formation of α-hydroxy-esters methyl lactate in the solutions while the other investigated methods fail to indicate this.

Near Infrared Spectrometric Investigations on the behaviour of Lactate.

In patients with life-threatening illnesses, the metabolic production and disposal of lactate are impaired, which leads to a build-up of blood lactate. In critical care units, the changes in lactate levels are measured through intermittent, invasive, blood sampling and in vitro assay. Continuous monitoring is lacking, yet such monitoring could allow early assessment of severity and prognosis to guide therapy. Currently, there is no routine means to measure lactate levels continuously, particularly non-invasively. The motivation of this study was to understand the interaction of lactate with light in the Near Infra Red (NIR) region of the electromagnetic spectrum. This was to create an opportunity to explore the possibility of a non-invasive sensing technology to monitor lactate continuously.In vitro studies were performed using solution samples with varying concentration levels of sodium lactate in isotonic Phosphate Buffer Solution (PBS) at constant pH (7.4). These samples were prepared using stoichiometric solution compositions and spectra for each sample were taken using a state-of-the-art spectrometer in the NIR region. The spectra were then analysed qualitatively by 2D correlation analysis, which identified the regions of interest. Further analysis of these regions using linear regression at four randomly selected wavelengths showed bathochromic shifts, which, moreover, showed systematic variation correlating with lactate concentration.

Association of p53 codon72 Arg>Pro polymorphism with susceptibility to nasopharyngeal carcinoma: evidence from a case-control study and meta-analysis.

Tumor suppressor p53 is a critical player in the fight against cancer as it controls the cell cycle check point, apoptotic pathways and genomic stability. It is known to be the most frequently mutated gene in a wide variety of human cancers. Single-nucleotide polymorphism of p53 at codon72 leading to substitution of proline (Pro) in place of arginine (Arg) has been identified as a risk factor for development of many cancers, including nasopharyngeal carcinoma (NPC). However, the association of this polymorphism with NPC across the published literature has shown conflicting results. We aimed to conduct a case-control study for a possible relation of p53 codon72 Arg>Pro polymorphism with NPC risk in underdeveloped states of India, combine the result with previously available records from different databases and perform a meta-analysis to draw a more definitive conclusion. A total of 70 NPC patients and 70 healthy controls were enrolled from different hospitals of north-eastern India. The p53 codon72 Arg>Pro polymorphism was typed by polymerase chain reaction, which showed an association with NPC risk. In the meta-analysis consisting of 1842 cases and 2330 controls, it was found that individuals carrying the Pro allele and the ProPro genotype were at a significantly higher risk for NPC as compared with those with the Arg allele and the ArgArg genotype, respectively. Individuals with a ProPro genotype and a combined Pro genotype (ProPro+ArgPro) also showed a significantly higher risk for NPC over a wild homozygote ArgArg genotype. Additionally, the strength of each study was tested by power analysis and genotype distribution by Hardy-Weinberg equilibrium. The outcome of the study indicated that both allele frequency and genotype distribution of p53 codon72 Arg>Pro polymorphism were significantly associated with NPC risk. Stratified analyses based on ethnicity and source of samples supported the above result.

Possible roles for prolactin, thyroxine and insulin in the growth of heifers before and during early pregnancy.

Two groups of British Friesian heifers with cyclic ovarian function and the potential for further growth were individually fed a restricted diet at two levels over a 12-week period. Eight heifers receiving the higher level of feed (SP group) and five receiving the lower level (LP group) became pregnant by artificial insemination at week 6. Mean (and SEM) body weight changes in the SP and LP groups were respectively 0.24 (+/- 0.024) kg day-1 and -0.19 (+/- 0.039) kg day-1. Over the period from weeks 3-12, mean concentrations of plasma prolactin, thyroxine and insulin in the five LP heifers were 39%, 67% and 74%, respectively, of those in the eight SP heifers. The results suggest that prolactin, thyroxine and insulin have roles in the growth of heifers before and during early pregnancy.

Effects of luteolytic doses of prostaglandin F2 alpha and cloprostenol on concentrations of progesterone, luteinizing hormone, follicle-stimulating hormone, glucose, insulin, growth hormone, thyroxine, prolactin and cortisol in jugular plasma of lactating dairy cows.

Three groups of five lactating dairy cows in the mid-luteal phases of oestrous cycles were given an injection (at time 0 h) of the naturally-occurring prostaglandin F2 alpha (PGF2 alpha) or cloprostenol (a synthetic analogue of PGE2 alpha) at doses recommended for inducing luteolysis, or injection vehicle. Concentrations of glucose and hormones in jugular plasma were measured from 26 h before to 12 h after the injections and the significance (P < 0.05) of the effects of the prostaglandins on these concentrations was determined. Both prostaglandins induced falls in progesterone concentration and rises in luteinizing hormone concentration; neither influenced follicle-stimulating hormone. PGF2 alpha increased glucose concentration; neither prostaglandin influenced insulin concentration. Both prostaglandins increased growth hormone concentration and resulted in declining thyroxine concentration. PGF2 alpha increased prolactin and cortisol concentration. There were, however, no significant differences between the effects of the two prostaglandins on any hormone (or glucose) concentration.